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ops.py
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ops.py
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import tensorflow as tf
import math
import numpy as np
#config e.g. dilations: [1,4,16,] In most cases[1,4,] is enough
def nextitnet_residual_block(input_, dilation, layer_id,
residual_channels, kernel_size,
causal=True, train=True):
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name,reuse=tf.AUTO_REUSE):
dilated_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="dilated_conv1"
)
input_ln = layer_norm(dilated_conv, name="layer_norm1", trainable=train)
#input_ln=tf.contrib.layers.layer_norm(dilated_conv,reuse=not train, trainable=train) #performance is not good, paramter wrong?
relu1 = tf.nn.relu(input_ln)
dilated_conv = conv1d(relu1, residual_channels,
2 *dilation, kernel_size,
causal=causal,
name="dilated_conv2"
)
input_ln = layer_norm(dilated_conv, name="layer_norm2", trainable=train)
#input_ln = tf.contrib.layers.layer_norm(dilated_conv, reuse=not train, trainable=train)
relu1 = tf.nn.relu(input_ln)
return input_ + relu1
# encoder-decoer
def nextitnet_residual_block_ED(input_, dilation, layer_id,
residual_channels, kernel_size,
causal=True, train=True, encoder=True):
if encoder==True:
resblock_type = "encoder"
else:
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name,reuse=tf.AUTO_REUSE):
dilated_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="dilated_conv1"
)
input_ln = layer_norm(dilated_conv, name="layer_norm1", trainable=train)
#input_ln=tf.contrib.layers.layer_norm(dilated_conv,reuse=not train, trainable=train) #performance is not good, paramter wrong?
relu1 = tf.nn.relu(input_ln)
dilated_conv = conv1d(relu1, residual_channels,
2 *dilation, kernel_size,
causal=causal,
name="dilated_conv2"
)
input_ln = layer_norm(dilated_conv, name="layer_norm2", trainable=train)
#input_ln = tf.contrib.layers.layer_norm(dilated_conv, reuse=not train, trainable=train)
relu1 = tf.nn.relu(input_ln)
return input_ + relu1
def get_adapter(input_,hidden_size=64):
with tf.variable_scope("adapters"):
conv_down = conv1d(input_, hidden_size,
name="adapter_conv1_down"
)
# relu2 = tf.nn.relu(conv1)
conv_down=gelu(conv_down)
residual_channels = input_.get_shape()[-1]
conv_up = conv1d(conv_down, residual_channels,
name="adapter_conv1_up"
)
return input_+conv_up
#Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Identity mappings in deep residual networks.
def nextitnet_residual_block_one(input_, dilation, layer_id,
residual_channels, kernel_size,
causal = True, train = True):
resblock_type = "decoder"
resblock_name = "nextitnet_residual_block_one_{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name):
input_ln = layer_norm(input_, name="layer_norm1", trainable = train)
relu1 = tf.nn.relu(input_ln)
conv1 = conv1d(relu1, int(0.5*residual_channels), name = "conv1d_1")
conv1 = layer_norm(conv1, name="layer_norm2", trainable = train)
relu2 = tf.nn.relu(conv1)
dilated_conv = conv1d(relu2, int(0.5*residual_channels),
dilation, kernel_size,
causal = causal,
name = "dilated_conv"
)
dilated_conv = layer_norm(dilated_conv, name="layer_norm3", trainable = train)
relu3 = tf.nn.relu(dilated_conv)
conv2 = conv1d(relu3, residual_channels, name = 'conv1d_2')
return input_ + conv2
#Conditional Image Generation with PixelCNN Decoders
def nextitnet_residual_block_gatedCNN(input_, dilation, layer_id,
residual_channels, kernel_size,
causal=True, train=True):
resblock_type = "decoder"
resblock_name = "gatedCNN_{}_layer_{}_{}".format(resblock_type, layer_id, dilation)
with tf.variable_scope(resblock_name):
dilated_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="dilated_conv"
)
tanh=tf.nn.tanh(dilated_conv)
gate_conv = conv1d(input_, residual_channels,
dilation, kernel_size,
causal=causal,
name="gate_conv"
)
sigm = tf.nn.sigmoid(gate_conv)
multi=tf.multiply(tanh,sigm)
multi=conv1d(multi, residual_channels, name="conv1d_1")
return input_ + multi
def conv1d(input_, output_channels,
dilation=1, kernel_size=1, causal=False,
name="dilated_conv"):
with tf.variable_scope(name):
weight = tf.get_variable('weight', [1, kernel_size, input_.get_shape()[-1], output_channels],
initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1))
bias = tf.get_variable('bias', [output_channels],
initializer=tf.constant_initializer(0.0))
if causal:
padding = [[0, 0], [(kernel_size - 1) * dilation, 0], [0, 0]]
padded = tf.pad(input_, padding)
input_expanded = tf.expand_dims(padded, dim=1)
out = tf.nn.atrous_conv2d(input_expanded, weight, rate=dilation, padding='VALID') + bias
else:
input_expanded = tf.expand_dims(input_, dim=1)
# out = tf.nn.conv2d(input_expanded, weight, strides=[1, 1, 1, 1], padding="SAME") + bias
out = tf.nn.atrous_conv2d(input_expanded, weight, rate=dilation, padding='SAME') + bias
return tf.squeeze(out, [1])
# tf.contrib.layers.layer_norm
def layer_norm(x, name, epsilon=1e-8, trainable=True):
with tf.variable_scope(name):
shape = x.get_shape()
beta = tf.get_variable('beta', [int(shape[-1])],
initializer=tf.constant_initializer(0), trainable=trainable)
gamma = tf.get_variable('gamma', [int(shape[-1])],
initializer=tf.constant_initializer(1), trainable=trainable)
mean, variance = tf.nn.moments(x, axes=[len(shape) - 1], keep_dims=True)
x = (x - mean) / tf.sqrt(variance + epsilon)
return gamma * x + beta
def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf